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A Student's Guide to Python for Physical Modeling

Second Edition

Jesse M. Kinder, Philip Nelson

PDF
ca. 24,99
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Princeton University Press img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Physik, Astronomie

Beschreibung

A fully updated tutorial on the basics of the Python programming language for science students

Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed.

This guide introduces a wide range of useful tools, including:

  • Basic Python programming and scripting
  • Numerical arrays
  • Two- and three-dimensional graphics
  • Animation
  • Monte Carlo simulations
  • Numerical methods, including solving ordinary differential equations
  • Image processing


Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.

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Schlagwörter

Computational complexity theory, Garbage collection (computer science), Notebook interface, File menu, Python syntax and semantics, Assignment (computer science), Object type (object-oriented programming), Text editor, Machine learning, Namespace, Error message, Computer scientist, Source code, String (computer science), Mathematical proof, Software, Version control, Programmer, Cell (microprocessor), Computer language, Wolfram Alpha, Processing (programming language), Calculation, Conda (package manager), Subroutine, Computer algebra system, Hidden file and hidden directory, Programming language, Pandas (software), Random number generation, IPython, Repository (version control), Rubber duck debugging, Python (programming language), Bash (Unix shell), Command-line interface, Expression (computer science), Debugger, Instruction set, Interpreter (computing), Nano (text editor), Keyboard shortcut, Python Package Manager, Mathematica, Snippet (programming), Web browser, Histogram, Installation (computer programs), MATLAB, Spreadsheet, Method (computer programming), Operating system, Anaconda (Python distribution), Typing, Name collision, Matplotlib, Computer programming, Conditional (computer programming), Scikit-learn, Data set, NumPy, Wildcard (Java), Application software, Numerical analysis, Parameter (computer programming), Cursor (user interface), Object-oriented programming, SymPy, Assembly language, Finder (software), Filename, Indentation (typesetting), For loop, Symbolic computation, MathJax, Snake case, Computer program, SciPy, Statement (computer science), Directory (computing), Clipboard (computing), Abstraction (software engineering), Comparison of programming languages (string functions), Computer lab, Encoder, Indent style, Coding (social sciences), Programming style, Docstring, Computer simulation, Instance (computer science), Memory management, Your Computer (British magazine), Tuple, Assertion (software development), Constructor (object-oriented programming), Machine code, Lab notebook, GitHub, Variable (computer science)